Abstract
Image thresholding (as the simplest form of segmentation) is a very challenging task because of the differences in the characteristics of different images such that different thresholds may be tried to obtain maximum segmentation accuracy. In this paper, a supervised neural network is used to “dynamically” threshold images by assigning a suitable threshold to each image. The network is trained using a set of simple features extracted from medical images randomly selected form a sample set and then tested using the remaining medical images. The results are compared with the Otsu algorithm and the active shape models (ASM) approach.
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References
Sezgin, B.S.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electronic Imaging 13, 146–165 (2004)
Otsu.: A threshold selection method from gray-level histograms. IEEE Trans., 62–66 (1979)
Bhandarkar, S.M., Koh, J., Suk, M.: A hierarchical neural network and its application to image segmentation. In: IMACS, pp. 337–355 (1996)
Ahmed, A.F.: Two-stage neural network for volume segmentation of medical images. PRL 18, 1143–1151 (1997)
Chang, P.C.: medical image segmentation using a contextual-constraint-based Hopfield neural cube. Image and Vision Computing 19, 669–678 (2001)
Kurugollu, F., Sankur, B., Harmanci, A.E.: Image segmentation by relaxation using constraint satisfaction neural network. Image and Vision Computing 20, 483–497 (2002)
Nuneza, J.L.: Astronomical image segmentation by self-organizing neural networks and wavelets. N.Ns. 16, 411–417 (2003)
Kurnaz, M.N., Dokur, Z., Ölmez, T.: An incremental neural network for tissue segmentation in ultrasound images. CMPB 85, 187–195 (2007)
Cootes, Hill, A., Taylor, C.J., Haslam, J.: The use of active shape models for locating structure in medical images. Image and Vision Computing 12, 355–366 (1994)
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Othman, A.A., Tizhoosh, H.R. (2010). A Neural Approach to Image Thresholding. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_72
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DOI: https://doi.org/10.1007/978-3-642-15819-3_72
Publisher Name: Springer, Berlin, Heidelberg
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